地理学报

地理学报 ›› 2017, Vol. 72 ›› Issue (12): 2241-2251.doi: 10.11821/dlxb201712009

• 交通物流 • 上一篇    下一篇

全球海洋运输网络健壮性评估

彭澎1,2(), 程诗奋1,2, 刘希亮1, 梅强3,4, 陆锋1()   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 集美大学航海学院,厦门 361021
    4. 船舶辅助导航技术国家地方联合工程研究中心,厦门 361021
  • 收稿日期:2017-02-22 修回日期:2017-10-18 出版日期:2017-12-25 发布日期:2017-12-26
  • 作者简介:

    作者简介：彭澎(1989-), 男, 博士生, 主要从事海上交通地理信息科学、复杂网络分析研究。E-mail: pengp@lreis.ac.cn

  • 基金资助:
    中国科学院重点项目(ZDRW-ZS-2016-6-3)

The robustness evaluation of global maritime transportation networks

Peng PENG1,2(), Shifen CHENG1,2, Xiliang LIU1, Qiang MEI3,4, Feng LU1()   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Navigation College of Jimei University, Xiamen 361021, Fujian, China
    4. National-local Joint Engineering Research Center for Marine Navigation Aids Services, Xiamen 361021, Fujian, China
  • Received:2017-02-22 Revised:2017-10-18 Online:2017-12-25 Published:2017-12-26
  • Supported by:
    Key Project of the Chinese Academy of Sciences, No.ZDRW-ZS-2016-6-3

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摘要：

海洋运输网络结构健壮性与运输效率密切相关,也是海洋运输系统抗干扰能力的具体表现。目前的海洋运输网络健壮性研究主要基于集装箱班轮运输网络开展,忽略了货运船舶的多态性。针对上述问题,本文利用2015年全球海洋货物运输船舶自动识别系统（Automatic Identification System, AIS）数据,构建了包含港口中转停留信息的全球海洋货物运输网络,然后从原油、集装箱、散货三种货物运输模式网络入手,证明其结构符合幂律分布,并采用随机性攻击和蓄意攻击策略,分析了三种网络在不同攻击策略下的破碎过程。结果表明：① 与基于集装箱班轮起止港口信息构建的运输网络相比,利用货运船舶AIS数据构建的运输网络更完整地反映了全球海洋运输格局和过程;② 不同货运网络结构健壮性存在巨大差异,散货运输网络最健壮,其次为原油运输网络,集装箱运输网络最脆弱;③ 小规模的蓄意攻击会对集装箱运输网络完整性产生较大的影响,而对散货运输网络和原油运输网络完整性影响较小。研究成果可为港口规划、航线设计与优化以及建立更可靠的海洋运输网络体系提供参考和决策支持。

关键词: 复杂网络, 海洋运输, 健壮性, 船舶自动识别系统

Abstract:

The structural robustness of maritime transportation network describes the anti-jamming ability of maritime transportation system, which is closely related to the transportation efficiency. Current researches on the robustness of maritime transportation networks mainly focus on the container transportation network, but ignore the type difference of cargo ships or even ports. This paper builds a more complete global maritime transportation network with the AIS data of the global cargo ships in 2015. Then, for the three transportation modes, namely oil tanker, container and bulk carrier, it proves that the three networks are complex networks with topological structures following the power law distribution, and three attack strategies including a random attack and two intentional attacks are conducted to evaluate the survivability of the corresponding transportation networks in different situations. The results show that: (1) in sharp comparison to the transportation network based on OD information of container liners, the networks constructed with the AIS data of the cargo ships fully reflect the global cargo transportation pattern and process; (2) The robustness of different maritime transportation networks differs greatly, with the container transportation network being the weakest and the bulk carrier transportation network the strongest. (3) Small intentional attacks may exert greater impact on the integrity of the container transportation network, but have less impact on bulk carrier transportation network and oil tanker transportation network. It is argued that these conclusions can help to improve decision support capabilities on maritime transportation planning and emergency response, which facilitates the establishment of a more reliable maritime transportation system.

Key words: complex network, maritime transportation, robustness, automatic identification system